The present invention relates to determining the output performance of a process unit in an oil refinery. Such units include pipestills, hydrocracking units, catalytic cracking units, hydroprocessing units, and reforming units. In particular, the present invention relates to a new and advanced process unit monitoring computer software (called xe2x80x9cunit monitoring toolsetxe2x80x9d) for specific process units.
A Unit Monitoring Toolset is an advanced monitoring computer software capability developed for specific process units that makes use of intelligent, automatic data collection, workup calculations, selective execution of process models to monitor and predict performance and provide input to assessments, reports, etc. The Toolset enables close monitoring, problem diagnosis, model tuning and assists in optimum operation identification, to the extent that the models themselves are capable of identifying these optimum operations. By bringing this to the contact engineer/unit engineer level, we are able to monitor the unit to the extent that best performance should be a daily event.
Current Monitoring Technology relies on monitoring plant measurements (flows, temperatures, etc.) and often comparing them to targets. The fundamental question of xe2x80x9cwhat is happening within an individual process unitxe2x80x9d is more complex than that described by such measurements. The Toolset provides process unit information for the user, rather than just measurements. Information comes from detailed calculations, analyses, data workup and the execution of simple or highly complex models which can simulate expected performance. Information like this provides an order of magnitude improvement in the ability to monitor a process unit.
The objectives of a Unit Monitoring Toolset include the following:
Provide state of the art monitoring capability;
Increase the frequency and sophistication at which model-based monitoring is performed;
Use detailed and often design-only models for routine unit monitoring and improvement;
Establish a means to automatically capture high quality data regarding process unit performance in a history database;
Perform calculations and/or run models and store key results in a database to provide a history of operating comparisons from which to use as a knowledge base for future operations;
Diagnose emerging problems sooner;
Replace or retire numerous standalone tools by consolidating them;
Provide better data interchange between analysis tools and components; and
Interact with/exploit desktop computing, engineering tools, and vendor plant information systems.
The present invention (hereinafter referred to as xe2x80x9cunit monitoring toolsetxe2x80x9d) is a method to monitor and analyze the performance of a hydrocarbon-processing unit such as a pipestill or hydrocracker unit. The method may also be used to monitor and analyze the performance of other refinery units including distillation units, hydrotreating units, catalytic cracking units, lubricating oil units and reforming units. For distillation units, the analysis uses equations that relate to the blending of feeds or different crude types, calculations of flash zone performance, hydraulic performance of tower sections, and hydrotreating. For hydrotreating units, the analysis uses equations that relate to catalyst performance and activation, and hydrogen purity. For catalytic cracking units, the analysis uses equations that relate to bed fluidization, catalyst circulation, catalyst additions, cracking estimations, emissions and regeneration. For lubricating oil units, the analysis uses equations that relate to extract and raffinate efficiency, composition impacts of qualities such as wax, additive use, and performance limits that impact qualities. For reforming units, the analysis uses equations that relate to catalyst performance, recycle gas quantity and quality, and regeneration effectiveness.
The invention includes the steps of collecting historical data relating to the hydrocarbon processing unit, from a process history database, validating the historical data, correcting the data, performing a workup to determine the output measurements for the hydrocarbon processing unit, and storing the results of the workup in the process history database. In a preferred embodiment, the historical data and the results of the workup are put into a process unit model for the oil refinery unit to determine an expected performance and potentially also an optimal performance.
The unit monitoring toolset has the following algorithm, shown in the overview FIG. 1, which includes the following steps:
1. Collecting data (as shown in FIG. 1, Step 2a) from the process history computer system (FIG. 1, Step 1a). In this regard, data refers to process instrument measurements, laboratory data, manually entered data, operational switches and stored constants for the unit. The collection is an intelligent matching of information from various sources and is a novel approach resulting in higher quality information.
2. Validating the data (FIG. 1, Step 3) by a set of logical rules (such as min/max checking, non-null and confidence checks, and other logical data validation rules such as increasing temperature boiling curves, etc.). This validation assures that the performance analysis is done on good data.
3. Performing a data workup (FIG. 1, Step 4), including a set of calculations that represent the sum total of all the experience of the process and operations experts in the organization for analyzing the operation of that unit. This calculation set yields many results which are all indicators of performance. The data workup varies for every type of process unit. Each Toolset workup is envisioned to be a distinct entity that can be installed, configured, upgraded or operated independently for each type of process unit. Toolset workups may share some underlying utilities or calculation modules, but since each process is fundamentally different, each Toolset workup will be unique.
4. Storing these workup results back into the same process history computer system (FIG. 1, Step 2b) where the original operating data was collected.
The toolset may also include the additional steps:
5. Collecting and inputting the data into a sophisticated process model that can be run to predict the expected operation of that unit (FIG. 1, part of step 3). This model contains the best technology from the organization or available commercially for that process. The models are often fundamental kinetic or molecular models but can also be correlation-based and are custom to the process unit. The models often require tuning, validation and customization to the unit being monitored. Calibration and tuning is often included as a part of the Toolset and may require independent calibration runs of the model in addition to the normal monitoring prediction runs. These models can also include anticipated or planned future operations as a part of the model prediction (for example, catalyst replacement planning requires anticipated future operations estimates).
6. Storing these model results back into the same process history computer system (FIG. 1, Step 2b) where the original operating data was collected.
7. Developing an effective set of reports and alerts (FIG. 1, Step 4) for monitoring (for example, hourly, daily, and weekly reports and exception reports for various peoplexe2x80x94plant operator, plant process engineer, central engineering expert, etc.).
The overall control of the entire process is achieved through the global attribute mapping (FIG. 1, Step 1b) kept in the Data Reference Attribute Table. This novel table holds the mapping and transposition master information that identifies how information is collected, transposed and moved throughout the various modules of the Toolset in a way that enables it to be automated and applicable to a wide variety of unit designs.